Modelling and Supporting Learners in Scientific Inquiry Exploratory Learning: A Bayesian Approach
Choo-Yee Ting, Mohammad Reza Beik Zadeh, Multimedia University, Malaysia
EdMedia + Innovate Learning, in Montreal, Canada ISBN 978-1-880094-56-3 Publisher: Association for the Advancement of Computing in Education (AACE), Waynesville, NC
Promoting learner's scientific inquiry skills and enhancing metacognition has been an ongoing challenge for intelligent scientific inquiry exploratory learning environment. It requires the modelling of both learners' currently level of domain knowledge and the scientific inquiry skills. InQPro, an intelligent scientific inquiry exploratory learning environment, refers to a probabilistic learner model to provide adaptive interventions tailored to the learner's knowledge and specific learning outcomes. InQPro learner model employs Bayesian networks that compute a probabilistic assessment of learner's scientific inquiry skills, and domain knowledge acquisition through learner's interactions with InQPro GUI, and Intelligent Pedagogical Agent. The discussion in this article is center around the metacognitive supports for hypotheses generation, and variables identification. This article will end with presenting preliminary investigation on employing Artificial Learners to study the interventions generated by the system in light of receiving learner's actions as evidences.
Ting, C.Y. & Beik Zadeh, M.R. (2005). Modelling and Supporting Learners in Scientific Inquiry Exploratory Learning: A Bayesian Approach. In P. Kommers & G. Richards (Eds.), Proceedings of ED-MEDIA 2005--World Conference on Educational Multimedia, Hypermedia & Telecommunications (pp. 4098-4104). Montreal, Canada: Association for the Advancement of Computing in Education (AACE).
© 2005 Association for the Advancement of Computing in Education (AACE)